Book Review of Cook, Sharon A. and Carson, Margaret. (2022). The Castleton Massacre: Survivors’ Stories of the Killins Femicide. Toronto: Dundurn Press.
Bibliographic record
Abstract
Stories of the Killins Femicide", the authors explore one of the most heinous crimes in Castleton, Ontario, where in 1963, Robert Killin murdered Florence Killin and three of their children.Instead of merely narrating the horrifying incidents, the writers critically analyze the systemic shortcomings that made such brutality possible.They accomplish this by combining historical study, social critique, and victim testimonies, concentrating on the patriarchal norms of the 1960s that put the protection of women and children last and the sanctity of the family first.The writers draw attention to the larger systemic and cultural problems at work by drawing on trauma studies and feminist philosophy.Robert's daughter Margaret Killins, who survived the massacre, provides testimony that frames the story.Her story, which is at the heart of the book, provides an emotional viewpoint on the tragedy, and the writers use it in conjunction with historical and sociological research to present a thorough analysis of the structural flaws that allowed this crime to occur.The book centers on Robert Killin who is a father, husband, and brother, who wielded control and violence to dominate his family.He thrived on having a hold on his estranged wife and children.He obsessively monitors their every move, using intimidation and fear to maintain a
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.042 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".